Abstract:

The use of artificial intelligence in higher education has been a topic of growing interest for scholars in recent years, as universities have increasingly integrated AI tools to support teaching and assessment processes. Its application has influenced student evaluation methods, academic performance measurement and learning analytics across different disciplines and institutional contexts. However, it is necessary to assess its pedagogical implications and the effectiveness of AI-assisted evaluations to understand their real impact on learning outcomes and to avoid potential biases or misuses. For this reason, the main goal of this literature review is to provide a comprehensive overview of empirical studies on the role of artificial intelligence in student assessment and evaluation within higher education. Based on 1,145 articles from the Web of Science (WoS) database, a relevant scientific database, this study explores extant research from 1987 to 2026. To provide a comprehensive literature review, this study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology used by Bel-Oms (2024), which allows researchers to search, review and identify studies focused on the main goal of this research. This research utilises a systematic review approach aligned with the PRISMA guidelines. The main results emphasise the importance of relevant factors in shaping the determination of evaluation in higher education when students use artificial intelligence and identify avenues for future research. Additionally, the review provides valuable insights for educators, researchers and policymakers seeking to develop effective assessment strategies and institutional policies that address the growing integration of artificial intelligence in university learning contexts.

Keywords:

Artificial Intelligence, Higher Education, Student Evaluation, Assessment Methods, Systematic Literature Review.